The detailed information of what exactly happened at checkout (for example, a retail checkout) can be extremely important. Existing approaches, however, generally infer such information by guesswork using a transaction log (TLOG), implementing human oversight at checkout, or implementing human oversight of the video of the checkout.
The TLOG only contains transactional events, and it misses such things as whether or not a customer, cashier and/or manager is present at any given time. A TLOG also misses a fake scan (that is, an item that is moved from the entry to exit area of the lane without being entered into the transaction), whether or not a customer's basket was empty as it left the checkout lane, as well as visual details of checkout events such as products scanned, people in customer/cashier area, etc.
Also, fraud is only committed in a small number of cases, and as the number of lanes to monitor increases, simply examining all of these events becomes disadvantageously time-consuming. Potential fraudulent events such as the above are of clear interest for retailers because they are often direct or indirect indicators of fraud committed by the customer and/or cashier and/or manager.